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Optimization Methods for Large-Scale Machine Learning

15 June 2016

Papers citing "Optimization Methods for Large-Scale Machine Learning"

50 / 866 papers shown

Title
Stochastic Coded Federated Learning: Theoretical Analysis and Incentive Mechanism Design Yuchang Sun Jiawei Shao Yuyi Mao Songze Li Jun Zhang FedML 91 8 0 08 Nov 2022
RCD-SGD: Resource-Constrained Distributed SGD in Heterogeneous Environment via Submodular Partitioning Haoze He Parijat Dube 58 1 0 02 Nov 2022
Convergence analysis of a quasi-Monte Carlo-based deep learning algorithm for solving partial differential equations Fengjiang Fu Xiaoqun Wang 36 2 0 28 Oct 2022
Preferential Subsampling for Stochastic Gradient Langevin Dynamics Srshti Putcha Christopher Nemeth Paul Fearnhead 59 0 0 28 Oct 2022
NeuralSearchX: Serving a Multi-billion-parameter Reranker for Multilingual Metasearch at a Low Cost Thales Sales Almeida Thiago Laitz Joao Seródio L. Bonifacio R. Lotufo Rodrigo Nogueira 53 4 0 26 Oct 2022
Decentralized Stochastic Bilevel Optimization with Improved per-Iteration Complexity Xuxing Chen Minhui Huang Shiqian Ma Krishnakumar Balasubramanian 92 29 0 23 Oct 2022
A note on diffusion limits for stochastic gradient descent Alberto Lanconelli Christopher S. A. Lauria DiffM 60 1 0 20 Oct 2022
Distributed Distributionally Robust Optimization with Non-Convex Objectives Yang Jiao Kai Yang Dongjin Song 64 12 0 14 Oct 2022
SARAH-based Variance-reduced Algorithm for Stochastic Finite-sum Cocoercive Variational Inequalities Aleksandr Beznosikov Alexander Gasnikov 90 2 0 12 Oct 2022
Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach Peng Mi Li Shen Tianhe Ren Yiyi Zhou Xiaoshuai Sun Rongrong Ji Dacheng Tao AAML 123 71 0 11 Oct 2022
Robust Graph Structure Learning via Multiple Statistical Tests Yaohua Wang Fangyi Zhang Ming Lin Senzhang Wang Xiuyu Sun Rong Jin 69 1 0 08 Oct 2022
Over-the-Air Federated Learning with Privacy Protection via Correlated Additive Perturbations Jialing Liao Zheng Chen Erik G. Larsson 89 13 0 05 Oct 2022
Taming Fat-Tailed ("Heavier-Tailed'' with Potentially Infinite Variance) Noise in Federated Learning Haibo Yang Pei-Yuan Qiu Jia Liu FedML 74 12 0 03 Oct 2022
Downlink Compression Improves TopK Sparsification William Zou H. Sterck Jun Liu 43 0 0 30 Sep 2022
FG-UAP: Feature-Gathering Universal Adversarial Perturbation Zhixing Ye Xinwen Cheng Xiaolin Huang AAML 110 11 0 27 Sep 2022
Robust Collaborative Learning with Linear Gradient Overhead Sadegh Farhadkhani R. Guerraoui Nirupam Gupta L. Hoang Rafael Pinot John Stephan FedML 82 16 0 22 Sep 2022
Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks Yunwen Lei Rong Jin Yiming Ying MLT 100 19 0 19 Sep 2022
Model-free Subsampling Method Based on Uniform Designs Mei Zhang Yongdao Zhou Zhengze Zhou Aijun Zhang 54 14 0 08 Sep 2022
Solving Elliptic Problems with Singular Sources using Singularity Splitting Deep Ritz Method Tianhao Hu Bangti Jin Zhi Zhou 91 6 0 07 Sep 2022
Cooperative coevolutionary Modified Differential Evolution with Distance-based Selection for Large-Scale Optimization Problems in noisy environments through an automatic Random Grouping Rui Zhong M. Munetomo 25 0 0 02 Sep 2022
Versatile Single-Loop Method for Gradient Estimator: First and Second Order Optimality, and its Application to Federated Learning Kazusato Oko Shunta Akiyama Tomoya Murata Taiji Suzuki FedML 94 0 0 01 Sep 2022
Flexible Vertical Federated Learning with Heterogeneous Parties Timothy Castiglia Shiqiang Wang S. Patterson FedML 128 36 0 26 Aug 2022
A simplified convergence theory for Byzantine resilient stochastic gradient descent Lindon Roberts E. Smyth 82 3 0 25 Aug 2022
A Graphical Model for Fusing Diverse Microbiome Data Mehmet Aktukmak Haonan Zhu M. Chevrette Julia Nepper S. Magesh J. Handelsman Alfred Hero 45 2 0 21 Aug 2022
Pandemic Control, Game Theory and Machine Learning Yao Xuan R. Balkin Jiequn Han Ruimeng Hu Héctor D. Ceniceros AI4CE 50 1 0 18 Aug 2022
Detection and Mitigation of Byzantine Attacks in Distributed Training Konstantinos Konstantinidis Namrata Vaswani Aditya Ramamoorthy AAML 83 0 0 17 Aug 2022
Exponential Concentration in Stochastic Approximation K. Law N. Walton Shan Yang 57 0 0 15 Aug 2022
Adaptive Learning Rates for Faster Stochastic Gradient Methods Samuel Horváth Konstantin Mishchenko Peter Richtárik ODL 63 9 0 10 Aug 2022
Quantization enabled Privacy Protection in Decentralized Stochastic Optimization Yongqiang Wang Tamer Basar 53 48 0 07 Aug 2022
Fixed-Point Automatic Differentiation of Forward--Backward Splitting Algorithms for Partly Smooth Functions Sheheryar Mehmood Peter Ochs 89 3 0 05 Aug 2022
SGEM: stochastic gradient with energy and momentum Hailiang Liu Xuping Tian 41 4 0 03 Aug 2022
An adjoint-free algorithm for conditional nonlinear optimal perturbations (CNOPs) via sampling Bin Shi Guodong Sun 22 5 0 01 Aug 2022
Analyzing Sharpness along GD Trajectory: Progressive Sharpening and Edge of Stability Z. Li Zixuan Wang Jian Li 97 47 0 26 Jul 2022
Approximation Power of Deep Neural Networks: an explanatory mathematical survey Mohammad Motamed 41 3 0 19 Jul 2022
Is Integer Arithmetic Enough for Deep Learning Training? Alireza Ghaffari Marzieh S. Tahaei Mohammadreza Tayaranian M. Asgharian V. Nia MQ 51 16 0 18 Jul 2022
On uniform-in-time diffusion approximation for stochastic gradient descent Lei Li Yuliang Wang 98 4 0 11 Jul 2022
Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation Alain Durmus Eric Moulines A. Naumov S. Samsonov 106 25 0 10 Jul 2022
Scalable K-FAC Training for Deep Neural Networks with Distributed Preconditioning Lin Zhang Shaoshuai Shi Wei Wang Yue Liu 70 10 0 30 Jun 2022
Meta-Wrapper: Differentiable Wrapping Operator for User Interest Selection in CTR Prediction Tianwei Cao Qianqian Xu Zhiyong Yang Qingming Huang 53 7 0 28 Jun 2022
Supervised Learning with General Risk Functionals Liu Leqi Audrey Huang Zachary Chase Lipton Kamyar Azizzadenesheli 57 5 0 27 Jun 2022
Theoretical analysis of Adam using hyperparameters close to one without Lipschitz smoothness Hideaki Iiduka 73 5 0 27 Jun 2022
Stochastic Langevin Differential Inclusions with Applications to Machine Learning F. Difonzo Vyacheslav Kungurtsev Jakub Mareˇcek 61 3 0 23 Jun 2022
GACT: Activation Compressed Training for Generic Network Architectures Xiaoxuan Liu Lianmin Zheng Dequan Wang Yukuo Cen Weize Chen ... Zhiyuan Liu Jie Tang Joey Gonzalez Michael W. Mahoney Alvin Cheung VLM GNN MQ 101 33 0 22 Jun 2022
On the Maximum Hessian Eigenvalue and Generalization Simran Kaur Jérémy E. Cohen Zachary Chase Lipton 115 43 0 21 Jun 2022
A Single-Timescale Analysis For Stochastic Approximation With Multiple Coupled Sequences Han Shen Tianyi Chen 110 15 0 21 Jun 2022
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates Yann Fraboni Richard Vidal Laetitia Kameni Marco Lorenzi FedML 54 25 0 21 Jun 2022
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data Timothy Castiglia Anirban Das Shiqiang Wang S. Patterson FedML 72 50 0 16 Jun 2022
Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning Anastasia Koloskova Sebastian U. Stich Martin Jaggi FedML 70 82 0 16 Jun 2022
On the fast convergence of minibatch heavy ball momentum Raghu Bollapragada Tyler Chen Rachel A. Ward 122 19 0 15 Jun 2022
Automatic Clipping: Differentially Private Deep Learning Made Easier and Stronger Zhiqi Bu Yu Wang Sheng Zha George Karypis 134 72 0 14 Jun 2022